Triple
T23712217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apollo 5 |
E585896
|
entity |
| Predicate | LMConfiguration |
P153663
|
FINISHED |
| Object | lunar module without landing legs deployed |
—
|
LITERAL FINISHED |
How this triple was built (2 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: lunar module without landing legs deployed | Statement: [Apollo 5, LMConfiguration, lunar module without landing legs deployed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: LMConfiguration Context triple: [Apollo 5, LMConfiguration, lunar module without landing legs deployed]
-
A.
hasLanguageModel
Indicates that an entity possesses, uses, or is associated with a particular language model.
-
B.
languageModel
Indicates that one entity is a language model used to process, generate, or understand natural language in relation to another entity.
-
C.
lineConfiguration
Indicates that the entities participate together in a specific arrangement or pattern along a line.
-
D.
languageProvision
Indicates that one entity supplies, supports, or makes available a particular language (or set of languages) for use by another entity.
-
E.
laterConfiguration
Indicates that one configuration occurs after another in time, representing a subsequent or later state relative to a prior configuration.
- F. None of above. chosen
Provenance (4 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_69e24905f77881908194d645676acd60 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b7784cd08190a442dd41d56b92b1 |
completed | April 29, 2026, 7:47 a.m. |
| PD | Predicate disambiguation | batch_69f155e4b1148190836ede4741dcb888 |
completed | April 29, 2026, 12:50 a.m. |
| PDg | Predicate description generation | batch_69f15b453da88190889a8d9b21727958 |
completed | April 29, 2026, 1:13 a.m. |
Created at: April 17, 2026, 6:54 p.m.