Triple
T11544125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zerg |
E273739
|
entity |
| Predicate | unitProductionStructure |
P99979
|
FINISHED |
| Object |
Hive
The Hive is the Zerg’s ultimate tech structure in StarCraft, enabling advanced units, upgrades, and late-game capabilities.
|
E930817
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Hive | Statement: [Zerg, unitProductionStructure, Hive]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hive Context triple: [Zerg, unitProductionStructure, Hive]
-
A.
Apache Hive
Apache Hive is a data warehouse and SQL-like query system built on top of Hadoop for managing and analyzing large datasets stored in distributed storage.
-
B.
HiveQL
HiveQL is a SQL-like query language designed for managing and analyzing large datasets stored in Apache Hive’s data warehouse system on Hadoop.
-
C.
Hive Metastore
Hive Metastore is a central metadata repository service that stores and manages schema and table information for data warehousing systems like Apache Hive.
-
D.
HiveServer2
HiveServer2 is a service component of Apache Hive that provides a secure, multi-client, and concurrent interface for executing Hive queries.
-
E.
IMPALA
IMPALA is a scalable deep reinforcement learning architecture designed for efficient distributed training of agents across many tasks and environments.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Hive Triple: [Zerg, unitProductionStructure, Hive]
Generated description
The Hive is the Zerg’s ultimate tech structure in StarCraft, enabling advanced units, upgrades, and late-game capabilities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hive Target entity description: The Hive is the Zerg’s ultimate tech structure in StarCraft, enabling advanced units, upgrades, and late-game capabilities.
-
A.
Apache Hive
Apache Hive is a data warehouse and SQL-like query system built on top of Hadoop for managing and analyzing large datasets stored in distributed storage.
-
B.
HiveQL
HiveQL is a SQL-like query language designed for managing and analyzing large datasets stored in Apache Hive’s data warehouse system on Hadoop.
-
C.
Hive Metastore
Hive Metastore is a central metadata repository service that stores and manages schema and table information for data warehousing systems like Apache Hive.
-
D.
HiveServer2
HiveServer2 is a service component of Apache Hive that provides a secure, multi-client, and concurrent interface for executing Hive queries.
-
E.
IMPALA
IMPALA is a scalable deep reinforcement learning architecture designed for efficient distributed training of agents across many tasks and environments.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d6aae4dfa48190a3ab0b19a159a3c5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d886e1d754819089f3b6be3404fa0b |
completed | April 10, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e685cc855881908ea96d84c76e3a4d |
completed | April 20, 2026, 8 p.m. |
| NEDg | Description generation | batch_69e68fd9d1b88190ba6057a52f51c961 |
completed | April 20, 2026, 8:43 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e69f3de9208190b65f25c3e2d6e222 |
completed | April 20, 2026, 9:48 p.m. |
Created at: April 8, 2026, 9:37 p.m.