Triple
T22766332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apollo 11 landing site |
E563136
|
entity |
| Predicate | firstWordsFromSurface |
P32464
|
FINISHED |
| Object | Houston, Tranquility Base here. The Eagle has landed. |
—
|
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: Houston, Tranquility Base here. The Eagle has landed. | Statement: [Apollo 11 landing site, firstWordsFromSurface, Houston, Tranquility Base here. The Eagle has landed.]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstWordsFromSurface Context triple: [Apollo 11 landing site, firstWordsFromSurface, Houston, Tranquility Base here. The Eagle has landed.]
-
A.
firstWordsOf
chosen
Indicates that one entity consists of the initial word or sequence of words taken from another entity (such as a text or utterance).
-
B.
hasSurfaceForm
Indicates that an abstract concept, entity, or linguistic unit is realized or expressed in a specific textual or lexical form.
-
C.
firstWord
Indicates that one entity is the first word in the sequence or text associated with another entity.
-
D.
hasFirstWordOfExpandedForm
Indicates that one entity is the first word in the fully expanded (non-abbreviated) form of another entity.
-
E.
loanwordsFrom
Indicates that one language has borrowed words from another language.
- F. None of above.
Provenance (3 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_69e24552e11c81909c2d61578a558bd7 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17a80e2688190b76844c408929d32 |
completed | April 29, 2026, 3:26 a.m. |
| PD | Predicate disambiguation | batch_69eed2b88d88819096015deb6a648801 |
completed | April 27, 2026, 3:06 a.m. |
Created at: April 17, 2026, 3:26 p.m.