Triple
T5394591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hallunda metro station |
E120619
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object |
HAD
HAD is the station code used to identify Hallunda metro station in the Stockholm Metro system.
|
E517333
|
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: HAD | Statement: [Hallunda metro station, hasStationCode, HAD]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HAD Context triple: [Hallunda metro station, hasStationCode, HAD]
-
A.
HAD
HAD is the commonly used abbreviation for the Historical Astronomy Division, a group focused on the study and promotion of the history of astronomy.
-
B.
HAF
HAF is the commonly used abbreviation for the Hellenic Air Force, the air warfare branch of Greece’s armed forces.
-
C.
HAV
HAV is the IATA airport code for José Martí International Airport, the main international gateway serving Havana, Cuba.
-
D.
HAS
HAS is the stock ticker symbol for Hasbro, Inc., a major American toy and entertainment company traded on the NASDAQ.
-
E.
HAJ
HAJ is the three-letter IATA airport code for Hannover Airport in Hanover, Germany.
- 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: HAD Triple: [Hallunda metro station, hasStationCode, HAD]
Generated description
HAD is the station code used to identify Hallunda metro station in the Stockholm Metro system.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: HAD Target entity description: HAD is the station code used to identify Hallunda metro station in the Stockholm Metro system.
-
A.
HAD
HAD is the commonly used abbreviation for the Historical Astronomy Division, a group focused on the study and promotion of the history of astronomy.
-
B.
HAF
HAF is the commonly used abbreviation for the Hellenic Air Force, the air warfare branch of Greece’s armed forces.
-
C.
HAV
HAV is the IATA airport code for José Martí International Airport, the main international gateway serving Havana, Cuba.
-
D.
HAS
HAS is the stock ticker symbol for Hasbro, Inc., a major American toy and entertainment company traded on the NASDAQ.
-
E.
HAJ
HAJ is the three-letter IATA airport code for Hannover Airport in Hanover, Germany.
- 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_69bd4637b92c8190b815b6443ae4b323 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd87441a208190b79561614759894b |
completed | March 20, 2026, 5:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf336d43a081909ab05d237c297c7b |
completed | March 22, 2026, 12:10 a.m. |
| NEDg | Description generation | batch_69bf33e3b9ac819091cb000a98a505cd |
completed | March 22, 2026, 12:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf349a746481909a8ba2a854e29449 |
completed | March 22, 2026, 12:15 a.m. |
Created at: March 20, 2026, 2:04 p.m.